1. Field of the Invention
The present invention relates generally to channel estimation, and more particularly to more robust and better performing channel estimation in an Orthogonal Frequency Division Multiplexing (OFDM) system.
2. Description of the Related Art
Modern cellular systems such as the 4th generation Long Term Evolution (LTE) and WiMax networks depend on coherent detection for data communications to achieve high performance, and coherent detection requires Channel State Information (CSI) to be implemented. For OFDM systems, pilot-aided channel estimation is an effective way to obtain CSI at the receiver side.
In one example of pilot-aided channel estimation (from LTE), predetermined Cell-specific Reference Signal (CRS) symbols known to the receiver are scattered in a regular pattern in the two-dimensional (2D) OFDM frequency-time plane and transmitted periodically on one or more antenna ports. At the receiver, typical channel estimation uses Least Square (LS) or Minimum Mean-Square Error (MMSE) calculations. In general, MMSE performs much better than LS and thus is often preferred in practice.
In the new LTE Release 10 specifications, a User Equipment (UE)-specific reference signal (RS) for demodulation is also introduced. See, e.g., 3GPP TS 36.211, “Evolved Universal Terrestrial Radio Access (E-UTRA): Physical Channels and Modulation (Release 10),” and 3GPP TS 36.213, “Evolved Universal Terrestrial Radio Access (E-UTRA): Physical layer procedures (Release 10),” both of which are incorporated in their entirety by reference. Such a dedicated RS (i.e., which is dedicated to a single UE) is referred to as a Demodulation Reference Signal (DMRS), and, because the same precoding (which can be non-codebook-based) can be applied to both the RS and data resource elements (REs), precoder-transparent demodulation is enabled—a feature unavailable with plain CRS. MMSE channel estimation calculations can also be used for DMRS. There are two key differences between DMRS and CRS: (1) DMRS is narrow-band, while CRS is wide-band; and (2) DMRS enables precoder-transparent demodulation by applying the same precoding (which can be non-codebook-based) on both RS and data resource elements (REs), while CRS does not.
Existing MMSE OFDM channel estimations typically fall into two types:                (1) the joint frequency-time 2D filter (hereinafter referred to as any of “2D”, “2D-MMSE”, “full 2D” or “f2D”, and “f2D-MMSE”); and        (2) the two 1D filters concatenated sequentially in the frequency and time direction (hereinafter referred to as “2×1D” and “2×1D-MMSE”).        
The 2D channel estimation has better performance at the cost of high computational complexity which also leads to large processing delay. The 2×1D method is a good tradeoff between complexity and performance and hence is often being implemented in practice. However, when there is a large delay spread and high Signal to Noise Ratio (SNR), there is a considerable performance gap between 2D and 2×1D. For more details on 2D and 2×1D, see any of the following references, each of which is incorporated herein in their entirety: U.S. Pat. Pub. No. 2012/0147761, entitled Channel Estimation for Long Term Evolution (LTE) Terminals; U.S. patent application Ser. No. 10/687,400, entitled Pilot-aided Channel Estimation for OFDM in Wireless Systems; and the Freescale Semiconductor Application Note entitled Channel estimation in OFDM systems, by Yushi Shen and Ed Martinez (2006).
Whether using 2D or 2×1D, MMSE estimators require current channel statistics, such as, e.g., channel power delay profile (PDP) and/or the Doppler spectrum. Such channel statistics can be estimated in some cases, such as when the CRS is continuously transmitted in the LTE system, but this necessarily causes extra complexity in the receiver. Usually the receiver does not directly estimate such channel statistics and instead relies on some reasonable assumptions.
It has been shown that when there is no true PDP knowledge at the receiver side, assuming a uniform PDP, i.e., that the channel power is evenly distributed in the maximum delay spread interval, is a robust choice in the sense that it can minimize the worse-case Mean-Square Error (MSE) asymptotically (namely, assuming an infinite number of pilots). The MSE degradation becomes highly insensitive to the mismatch between the real current PDP and the assumed uniform PDP model. Hence, the uniform PDP is the default choice from both approximate and heuristic aspects. For more details, see Pilot-symbol-aided channel estimation for OFDM in wireless systems, by Ye Li, et al., IEEE Trans. Veh. Technol., vol. 49, No. 4, July 2000, the entire contents of which are incorporated herein in its entirety.
However, it is very difficult, and in some cases impossible, to estimate the PDP using the UE-specific DMRS defined in the LTE standard, which has precoding which may change from subframe to subframe and PRB (Physical Resource Block) to PRB. The differences between the UE-specific reference signal DMRS and the cell-specific reference signal CRS cause some problems unique to DMRS:                Unlike wide-band CRS, denoising in the time domain is not a viable approach to reduce the noise level for DMRS channel estimation.        Unlike CRS, in which MMSE weights can be derived from estimated channel statistics (i.e., PDP), estimated channel statistics like PDP are not suitable for DMRS because DMRS precoding may change from PRB to PRB and/or from subframe to subframe.        The usually robust uniform distribution PDP model incurs mismatch loss when used for DMRS channel estimation especially at high SNR with large delay spreads.        In addition to the knowledge or assumption of PDP, the MMSE estimator also demands the information of equivalent SNR. For CRS, the SNR can be estimated with reasonable accuracy. However, for DMRS, there may be some power mismatch between CRS and DMRS due to precoding. Such a mismatch will result in unavoidable performance loss as well.        For CRS, different antenna ports are orthogonal in time and frequency and there are no interferences among them. For DMRS in a Multiple User Multiple Input Multiple Output (MU-MIMO) environment, two antenna ports or more may use non-orthogonal random sequences in generating DMRS sequences, resulting in cross interferences among them. Joint channel estimation or interference cancellation should be used if such interferences are strong.        
Thus, there is a need for systems, devices, and methods for OFDM channel estimation with greater performance, reliability, and robustness.